MobiCrowd: Mobile Crowdsourcing On Location-based Social Networks

Authors:
Yulong Tian Nanjing University, P.R. China
Wei Wei College of William and Mary, USA
Qun Li College of William and Mary, USA
Fengyuan Xu Nanjing University, P.R. China
Sheng Zhong Nanjing University, P.R. China

Abstract:

The great potential of mobile crowdsourcing has started to attract attention of both industries and the research community. However, current commercial mobile crowdsourcing marketplaces are unsatisfactory because of the limited worker base and functionality. In this paper, we first revisit the foundation of performing mobile crowdsourcing on location-based social networks (LBSNs) through specially designed survey studies and comparison experiments involving hundreds of users. Our results reveal that active check-ins are good indicators of picking a right user to perform tasks, and LBSN could be an ideal platform for mobile crowdsourcing given proper services provided. We then propose both the centralized and decentralized design of MobiCrowd, a mobile crowdsourcing service built on LBSNs. Our evaluation, through trace-driven simulation and real-world experiments, demonstrates that the proposed schemes can effectively find workers for mobile crowdsourcing tasks associated with different venues by analyzing their location check-in histories.

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